[PYTHON] Visualization of mixed matrices using sklearn.metrics.ConfusionMatrixDisplay

Confusion matrices can be easily visualized using scikit-learn, but sklearn.metrics.plot_confusion_matrix is * * estimator is required as an argument **. When I was searching for a method that does not require an estimator because it only visualizes, I found screarn.metrics.ConfusionMatrixDisplay. So I wrote the code easily.


import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

data = load_breast_cancer()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = SVC(random_state=0)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
cm = confusion_matrix(y_pred=y_pred, y_true=y_test)
cmp = ConfusionMatrixDisplay(cm, display_labels=data.target_names)

cmp.plot(cmap=plt.cm.Blues)

The result looks like this. ダウンロード.png

reference

Recommended Posts

Visualization of mixed matrices using sklearn.metrics.ConfusionMatrixDisplay
Basics of Tableau Basics (Visualization Using Geographic Information)
[Python] Implementation of clustering using a mixed Gaussian model
Two-dimensional visualization of document vectors using Word2Vec trained model
Example of using lambda
Using MLflow with Databricks ② --Visualization of experimental parameters and metrics -
Implementation of TF-IDF using gensim
Visualization of data by prefecture
python: Basics of using scikit-learn ①
About machine learning mixed matrices
Data visualization method using matplotlib (1)
EM of mixed Gaussian distribution
Introduction of caffe using pyenv
Data visualization method using matplotlib (2)
Visualization of possessed skills [continuation]
A memorandum of using eigen3